David Nicholson (https://nicholdav.info/) is a neuroscientist at Emory University in Atlanta, Georgia. He works for the Prinz lab in the Biology department, developing brain-inspired continual machine learning algorithms as a member of a multi-university team on a DARPA project. He also works in applied machine learning in the area of animal vocalizations. In collaboration with Yarden Cohen, he developed a library to help researchers use neural networks for automated segmentation and annotation of vocalizations (https://github.com/NickleDave/vak). They have used this library to benchmark the first neural net architecture capable of accurately segmenting and labeling syllables in hundreds of hours of complex birdsong, such as that of the canary (https://github.com/yardencsGitHub/tweetynet). These projects began during his graduate studies in Sam Sober's lab at Emory, where his dissertation work showed that connections known to be important for learning motor skills in humans and other mammals are also found in regions of the songbird brain that are required to learn song. Lastly, David maintains several other Python packages and tools related to his research (more at https://github.com/NickleDave/MetaNickleDave).